Study on the Level of Talent Attractiveness of the Yangtze River Delta Urban Agglomerations Using Bayesian Quantile Regression Method

Abstract

Innovation is the first driving force to lead development, and the carrier of innovation drive is talent, and talent has been an important strategic resource to lead social development in the 21 st century. And different levels of cities should focus on the strategy of attracting talents. In this paper, based on the index data of the Yangtze River Delta Urban Agglomerations in 2019, we construct the evaluation talent attractiveness level score and use it to build a Bayesian quantile regression model to map the focus that cities of different levels should focus on talent attraction policies. The results show that the marginal gain of talent attraction is different for different level cities in different dimensions. By quantifying the level of talent attractiveness of different cities through the objective assignment method, we find the differences in the spatial distribution of talent attractiveness of different cities in the Yangtze River Delta region and provide theoretical guidance for the integration of the Yangtze River Delta. At the same time, by exploring the differences in the talent attractiveness of cities of different levels that should be focused on, we find the general rules and provide reference and guidance for other cities.